Milo: liver cirrhosis analysis

Introduction

In this notebook we demonstrate how to use Milo to detect abherrant cell states in diseased tissues, using a dataset of hepatic non-parenchymal cells isolated from 5 healthy and 5 cirrhotic human livers. Ramachandran et al. 2019 (GEO accessiion: GSE136103).

Installation

Added by DRT 2021-12-03

Updated by DRT 2023-11-29

source("getReqdPkgs.r")

pkgs <- c("SingleCellExperiment","scater","scran","Seurat","miloR",
  "tidyverse","patchwork","igraph","ggplot2",
  "ggrastr","msigdbr","clusterProfiler",
  "RColorBrewer","cowplot","devtools")

extra_pkgs <- c("irlba","Matrix") # NOTE these must be installed from source to make code work

invisible(getReqdPkgs(pkgs))

'getOption("repos")' replaces Bioconductor standard repositories, see 'help("repositories", package = "BiocManager")' for
details.
Replacement repositories:
    CRAN: https://cran.rstudio.com/
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NOTE

As of 2023-11-29, the runPCA function in scater breaks due to change in Matrix library; must downgrade to Matrix 1.6.1 Downloaded v 1.6.1 from https://cran.r-project.org/src/contrib/Archive/Matrix/

See this link: https://github.com/bwlewis/irlba/issues/70

# install.packages("Matrix", type = "source")
# install.packages("irlba", type = "source")

library(SingleCellExperiment)

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Welcome to Bioconductor

    Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see
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library(scater)

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library(scran)
library(miloR)

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library(tidyverse)

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library(patchwork)
library(igraph)

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library(ggplot2)
library(ggrastr)
library(msigdbr)
library(cowplot)

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library(Matrix)

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library(irlba)
library(RColorBrewer)

Load data

We downloaded the dataset and annotations stored in Seurat object from here, as indicated by the authors.

# load("/nfs/team205/ed6/data/Ramachandran2019_liver/tissue.rdata")
load(url("https://www.dropbox.com/s/bq816h74gmh84gu/tissue.rdata?dl=1"))

Loading required package: Seurat
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## Convert to SingleCellExperiment
liver_sce <- SingleCellExperiment(assay = list(counts=tissue@raw.data, logcounts=tissue@data),
                                  colData = tissue@meta.data)

liver_sce

class: SingleCellExperiment 
dim: 23498 58358 
metadata(0):
assays(2): counts logcounts
rownames(23498): FO538757.2 AP006222.2 ... CTA-126B4.7 LINC01423
rowData names(0):
colnames(58358): Healthy1_Cd45+_AAACCTGCAGTATCTG Healthy1_Cd45+_AACTGGTTCATGGTCA ...
  Cirrhotic3_Cd45-_TTTGTCATCCAGGGCT Cirrhotic3_Cd45-_TCTGGAAGTCATCCCT
colData names(10): nGene nUMI ... annotation_indepth annotation_lineage
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):

Preprocessing

We use the same number of highly variable genes and principal components used by the authors of the original study.

Feature selection

Select highly variable genes

dec_liver <- modelGeneVar(liver_sce)

fit_liver <- metadata(dec_liver)
plot(fit_liver$mean, fit_liver$var, xlab="Mean of log-expression",
    ylab="Variance of log-expression")

hvgs <- getTopHVGs(dec_liver, n=3000)

Dimensionality reduction

set.seed(42)
liver_sce <- runPCA(liver_sce, subset_row=hvgs, ncomponents=11)
liver_sce <- runUMAP(liver_sce, dimred="PCA", ncomponents=2)

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scater::plotUMAP(liver_sce, colour_by="condition", point_alpha=1,  point_size=0.5)

scater::plotUMAP(liver_sce, colour_by="dataset", point_alpha=0.3,  point_size=0.5)

scater::plotUMAP(liver_sce, colour_by="annotation_lineage", point_alpha=0.3,  point_size=0.5, text_by='annotation_lineage')

Notably, this dataset doesn’t appear to display a batch effect

Note: This step is not required. It generates a large file (even larger than raw data) and then reloads it.

# saveRDS(liver_sce, "~/dropbox-vu/temp/milo_data/Ramachandran2019_liver/liver_SCE_20210225.RDS")
# liver_sce <- readRDS(url("https://www.dropbox.com/s/z0aopf616b1urvj/liver_SCE_20210225.RDS?dl=1"))

Differential Abundance analysis with Milo

We test for differential abundance between healthy and cirrhotic livers. We start by defining neighbourhoods with refined sampling on the KNN graph. We inspect the size of neighbourhoods.

liver_milo <- Milo(liver_sce)

## Build KNN graph
liver_milo <- buildGraph(liver_milo, d = 11, k=30)

Constructing kNN graph with k:30

## Compute neighbourhoods with refined sampling
liver_milo <- makeNhoods(liver_milo, k=30, d=11, prop = 0.05, refined=TRUE)

Checking valid object
Running refined sampling with reduced_dim

plotNhoodSizeHist(liver_milo, bins=150)

Then we make a design matrix for the differential test, assigning samples to biological conditions.

colData(liver_milo)[['sort']] <- str_remove(colData(liver_milo)[['dataset']], ".+_")
colData(liver_milo)[['sort']] <- str_remove(colData(liver_milo)[['sort']], "A|B")

liver_meta <- as_tibble(colData(liver_milo)[,c("dataset","condition", 'sort')])
liver_meta <- distinct(liver_meta) %>%
  mutate(condition=factor(condition, levels=c("Uninjured", "Cirrhotic"))) %>%
  column_to_rownames("dataset")

Now we can count cells in neighbourhoods and perform the DA test.

liver_milo <- countCells(liver_milo, samples = "dataset", meta.data = data.frame(colData(liver_milo)[,c("dataset","condition",'sort')]) )

Checking meta.data validity
Counting cells in neighbourhoods

liver_milo <- calcNhoodDistance(liver_milo, d=11)

'as(<dgTMatrix>, "dgCMatrix")' is deprecated.
Use 'as(., "CsparseMatrix")' instead.
See help("Deprecated") and help("Matrix-deprecated").

milo_res <- testNhoods(liver_milo, design = ~ condition, design.df = liver_meta[colnames(nhoodCounts(liver_milo)),])

Using TMM normalisation
Performing spatial FDR correction withk-distance weighting

milo_res_sort <- testNhoods(liver_milo, design = ~ sort + condition, design.df = 
                                liver_meta[colnames(nhoodCounts(liver_milo)),])

Using TMM normalisation
Performing spatial FDR correction withk-distance weighting

compare_da_df <- left_join(milo_res_sort, milo_res, by="Nhood", suffix=c("_sort", "_nosort")) %>%
  {annotateNhoods(liver_milo, ., 'annotation_lineage')} 

compare_da_df %>%
  ggplot(aes(-log10(SpatialFDR_sort), -log10(SpatialFDR_nosort))) +
  geom_point(size=0.8) +
  geom_point(data=. %>% filter(annotation_lineage=="Endothelia"), color="red")

plot(milo_res_sort$SpatialFDR, milo_res$SpatialFDR)

Exploration of Milo DA results

We can start by looking at some basic stats

pval_hist <- milo_res %>%
  ggplot(aes(PValue)) +
  geom_histogram(bins=50) +
  theme_bw(base_size=14)

volcano <-
  milo_res %>%
  ggplot(aes(logFC, -log10(SpatialFDR))) +
  geom_point(size=0.4, alpha=0.2) +
  geom_hline(yintercept = -log10(0.1)) +
  xlab("log-Fold Change") +
  theme_bw(base_size=14)

pval_hist + volcano

The distribution of P-values looks sensible and from the volcano plot we can see that we have identified some DA neighbourhoods at 10% FDR.

We can visualize DA neighbourhoods building an abstracted graph

liver_milo <- buildNhoodGraph(liver_milo)
plotNhoodGraphDA(liver_milo, milo_res, alpha = 0.1, size_range=c(2,6))

Note: This step is not required. It generates a large file (even larger than raw data) and then reloads it.

## Save milo object and results
# saveRDS(liver_milo,"~/dropbox-vu/temp/milo_data/Ramachandran2019_liver/liver_milo_20210225.RDS")
# write_csv(milo_res,"~/dropbox-vu/temp/milo_data/Ramachandran2019_liver/liver_results_20210225.csv")

# liver_milo <- readRDS("~/liver_milo_20201008.RDS")
liver_milo <- readRDS(url("https://www.dropbox.com/s/xdp07789c5hoen3/liver_milo_20210225.RDS?dl=1"))
# milo_res <- read_csv("/nfs/team205/ed6/data/Ramachandran2019_liver/liver_results_20201008.csv")
milo_res <- read_csv(url("https://www.dropbox.com/s/i1l3aep1py5wirf/liver_results_20210225.csv?dl=1"))

NOTE: hvgs (presumably highly variable genes) cannot be loaded since file not provided

## Load hvgs 
# hvgs <- scan("~/data/Ramachandran2019_liver/liver_milo_hvgs.txt", "")

Attempting to generate a set of highly variable genes

#

Making figures for the manuscript

colourCount = length(unique(liver_milo$annotation_lineage))
getPalette = colorRampPalette(brewer.pal(9, "Set2"))

Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

umap_df <- data.frame(reducedDim(liver_milo, "UMAP"))
colnames(umap_df) <- c("UMAP_1", "UMAP_2")

umap1 <- cbind(umap_df, annotation_lineage=liver_milo$annotation_lineage) %>%
  ggplot(aes(UMAP_1, UMAP_2, color=as.character(annotation_lineage))) +
  geom_point_rast(size=0.1, alpha=0.5, raster.dpi = 800) +
  ggrepel::geom_text_repel(data = . %>%
              group_by(annotation_lineage) %>%
              summarise(UMAP_1=mean(UMAP_1), UMAP_2=mean(UMAP_2)),
            aes(label=annotation_lineage), color="black", size=6
            ) +
  scale_color_manual(values=getPalette(colourCount)) +
  guides(color="none") +
  xlab("UMAP1") + ylab("UMAP2") +
  coord_fixed() +
  theme_classic(base_size = 22) +
  theme(axis.text = element_blank(), axis.ticks = element_blank())
ggsave("~/dropbox-vu/temp/milo_output/liver_v2/liver_umap1_new.pdf", height = 7, width = 8)


umap2 <-
  cbind(umap_df, condition=as.character(liver_milo$condition)) %>%
  ggplot(aes(UMAP_1, UMAP_2, color=condition)) +
  geom_point_rast(size=0.1, alpha=0.5, raster.dpi = 800) +
  scale_color_brewer(palette="Set1", name='') +
  xlab("UMAP1") + ylab("UMAP2") +
  coord_fixed() +
  guides(color='none') +
  facet_wrap(condition~., ncol=1) +
  theme_nothing(font_size = 22) +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), legend.position=c(0.9,0.9),
        strip.background = element_rect(color=NA), strip.text = element_text(size=22))
ggsave("~/dropbox-vu/temp/milo_output/liver_v2/liver_umap2_new.pdf", height = 7, width = 8)


nh_graph_pl <- plotNhoodGraphDA(liver_milo, milo_res, alpha = 0.1, size_range=c(1,4)) +
  theme(legend.text = element_text(size=20), legend.title = element_text(size=22)) +
  coord_fixed()

# nh_graph_pl + ggsave("~/mount/gdrive/milo/Figures/liver_v2/liver_graph.pdf", height = 7, width = 8)
# ggsave("~/dropbox-vu/temp/milo_output/liver_v2/liver_graph.pdf", height = 7, width = 8)

ggsave("~/dropbox-vu/temp/milo_output/liver_v2/liver_graph_new.pdf", height = 7, width = 8)

fig4_top <- (umap1 | umap2 | nh_graph_pl) +
  plot_layout(widths = c(3,1,3))

fig4_top

Explore DA neighbourhoods by cell type

Next, we can check the cell types where we observe most differences between healthy and cirrhotic cells, by taking the most frequent cell type in each neighbourhood.

milo_res <- milo_res[,!str_detect(colnames(milo_res), "annotation_lineage")]

# Add annotation of most frequent cell type per nhood to milo results table
milo_res <- annotateNhoods(liver_milo, milo_res, "annotation_indepth")
anno_df <- data.frame(liver_milo@colData) %>%
  distinct(annotation_lineage, annotation_indepth)
milo_res <- left_join(milo_res, anno_df, by="annotation_indepth")

We first check that neighbourhoods are sufficiently homogeneous

frac_hist <- ggplot(milo_res, aes(annotation_indepth_fraction)) +
  geom_histogram(bins=30) +
  xlab("Fraction of cells in \nmost abundant cluster") +
  ylab("# neighbourhoods") +
  theme_bw(base_size=14)

frac_hist

Filter nhoods with homogeneous composition

milo_res$annotation_indepth[milo_res$annotation_indepth_fraction < 0.6] <- NA
milo_res$annotation_lineage[milo_res$annotation_indepth_fraction < 0.6] <- NA

I can recover all the clusters where DA was detected in the original paper

group.by = "annotation_indepth"
paper_DA <- list(cirrhotic=c("MPs (4)","MPs (5)",
                             "Endothelia (6)", "Endothelia (7)",
                             "Mes (3)",
                             "Tcells (2)",
                             "Myofibroblasts"
                             ),
                 healthy=c("MPs (7)",
                           "Endothelia (1)",
                           "Tcells (1)", "Tcells (3)","Tcells (1)",
                           "ILCs (1)"
                           )
                 )

expDA_df <- bind_rows(
  data.frame(annotation_indepth = paper_DA[["cirrhotic"]], pred_DA="cirrhotic"),
  data.frame(annotation_indepth = paper_DA[["healthy"]], pred_DA="healthy")
  )

pl1 <- milo_res %>%
  left_join(expDA_df) %>%
  mutate(is_signif = ifelse(SpatialFDR < 0.1, 1, 0)) %>%
  mutate(logFC_color = ifelse(is_signif==1, logFC, NA)) %>%
  arrange(annotation_lineage) %>%
  mutate(Nhood=factor(Nhood, levels=unique(Nhood))) %>%
  filter(!is.na(annotation_lineage)) %>%
  ggplot(aes(annotation_indepth, logFC, color=logFC_color)) +
  scale_color_gradient2() +
  guides(color="none") +
  xlab(group.by) + ylab("Log Fold Change") +
  ggbeeswarm::geom_quasirandom(alpha=1) +
  coord_flip() +
  facet_grid(annotation_lineage~., scales="free", space="free") +
  theme_bw(base_size=22) +
  theme(strip.text.y =  element_text(angle=0),
        axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(),
        )

pl2 <- milo_res %>%
  left_join(expDA_df) %>%
  # dplyr::filter(!is.na(pred_DA)) %>%
  group_by(annotation_indepth) %>%
  summarise(pred_DA=dplyr::first(pred_DA), annotation_lineage=dplyr::first(annotation_lineage)) %>%
  mutate(end=ifelse(pred_DA=="healthy", 0, 1),
         start=ifelse(pred_DA=="healthy", 1, 0)) %>%
  filter(!is.na(annotation_lineage)) %>%
  ggplot(aes(annotation_indepth, start, xend = annotation_indepth, yend = end, color=pred_DA)) +
  geom_segment(size=1,arrow=arrow(length = unit(0.1, "npc"), type="closed")) +
  coord_flip() +
  xlab("annotation") +
  facet_grid(annotation_lineage~.,
    # annotation_lineage~"Ramachandran et al.\nDA predictions",
             scales="free", space="free") +
  # guides(color="none") +
  scale_color_brewer(palette="Set1", direction = -1,
                     labels=c("enriched in cirrhotic", "enriched in healthy"),
                     na.translate = F,
                     name="Ramachandran et al.\nDA predictions") +
  guides(color=guide_legend(ncol = 1)) +
  theme_bw(base_size=22) +
  ylim(-0.1,1.1) +
  theme(strip.text.y = element_blank(),strip.text.x = element_text(angle=90),
        plot.margin = unit(c(0,0,0,0), "cm"), panel.grid = element_blank(),
        axis.title.x = element_blank(), axis.text.x = element_blank(), axis.ticks.x = element_blank(),
        legend.position = "bottom")

fig4_bleft <- (pl2 + pl1 +
  plot_layout(widths=c(1,10), guides = "collect") & theme(legend.position = 'top', legend.justification = 0))

# ggsave("~/mount/gdrive/milo/Figures/liver_v2/liver_DAcomparison.pdf", width=8, height = 13)
# ggsave("~/dropbox-vu/temp/milo_output/liver_v2/liver_DAcomparison.pdf", width=8, height = 13)

pl2

Close-up on Endothelial lineage

endo_milo <- scater::runUMAP(liver_milo[,liver_milo$annotation_lineage=="Endothelia"],  dimred='PCA')

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plotUMAP(endo_milo, colour_by = "annotation_indepth")

umap_df <- data.frame(reducedDim(endo_milo, "UMAP"))
colnames(umap_df) <- c("UMAP_1", "UMAP_2")

endo_umap <- cbind(umap_df, condition=endo_milo$condition) %>%
   ggplot(aes(UMAP_1, UMAP_2, color=condition)) +
  geom_point(size=0.3, alpha=0.5) +
  scale_color_brewer(palette="Set1", name='') +
  xlab("UMAP1") + ylab("UMAP2") +
  coord_fixed() +
  guides(color="none") +
  facet_wrap(condition~., ncol=1) +
  theme_nothing() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), legend.position=c(0.9,0.9),
        strip.background = element_rect(color=NA), strip.text = element_text(size=22))

liver_milo2 <- liver_milo
subset.nhoods <- str_detect(milo_res$annotation_indepth, "Endo")
reducedDim(liver_milo2, "UMAP")[colnames(endo_milo),] <- reducedDim(endo_milo, "UMAP") 

endo_gr <-
  plotNhoodGraphDA(
  liver_milo2, milo_res,
  subset.nhoods = which(milo_res$annotation_lineage == "Endothelia"), 
  size_range=c(1,4),
  # ) =)[1:(length()-1)], 
  alpha = 0.1
  )  +
   theme(legend.text = element_text(size=20), legend.title = element_text(size=22))
  
# liver_milo2 <- liver_milo
# subset.nhoods <- str_detect(milo_res$annotation_indepth, "Endo")
# reducedDim(liver_milo2, "UMAP")[colnames(endo_milo),] <- reducedDim(endo_milo, "UMAP") 
# endo_gr_groups <- plotNhoodGroups(liver_milo2, milo_res_endogroups[milo_res_endogroups$annotation_lineage=="Endothelia",], 
#                 show_groups = c("54", "70"),
#                 size_range=c(1,4),
#                 subset.nhoods = milo_res_endogroups$annotation_lineage=="Endothelia") +
#   scale_fill_manual(values=c("54"=brewer.pal(4, "Spectral")[2], "70"=brewer.pal(4, "Spectral")[3]), 
#                     labels=c("54"="Uninjured group", '70'= "Cirrhotic group"),
#                     na.value="white",
#                     name = "Nhood group"
#                     ) +
#   theme(legend.text = element_text(size=20), legend.title = element_text(size=22))

fig4_bright1 <- ((endo_umap + endo_gr ) + 
  plot_layout(widths = c(1,2), 
                guides = "collect"
                )) 
fig4_bright1

Close-up on Cholangiocytes

chol_milo <- scater::runUMAP(liver_milo[,liver_milo$annotation_lineage=="Cholangiocytes"],  dimred='PCA')

Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’

plotUMAP(chol_milo, colour_by = "annotation_indepth")

plotUMAP(chol_milo, colour_by = "percent.mito")

Filter out cells that show contamination from immune cells (expression of immune markers)

keep <- logcounts(chol_milo)["CD19",] == 0 | logcounts(chol_milo)["MS4A1",] == 0
chol_milo <- chol_milo[,keep]
chol_milo <- scater::runUMAP(chol_milo,  dimred='PCA')

Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’

plotUMAP(chol_milo, colour_by = "annotation_indepth")

umap_df <- data.frame(reducedDim(chol_milo, "UMAP"))
colnames(umap_df) <- c("UMAP_1", "UMAP_2")

chol_umap <- cbind(umap_df, condition=chol_milo$condition) %>%
   ggplot(aes(UMAP_1, UMAP_2, color=condition)) +
  geom_point(size=0.3, alpha=0.5) +
  scale_color_brewer(palette="Set1", name='') +
  xlab("UMAP1") + ylab("UMAP2") +
  coord_fixed() +
  guides(color="none") +
  facet_wrap(condition~., ncol=1) +
  theme_nothing() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), legend.position=c(0.9,0.9),
        strip.background = element_rect(color=NA), strip.text = element_text(size=22))

chol_umap

liver_milo2 <- liver_milo
subset.nhoods <- milo_res$annotation_lineage=="Cholangiocytes"
reducedDim(liver_milo2, "UMAP")[colnames(chol_milo),] <- reducedDim(chol_milo, "UMAP") 

chol_gr <-
  plotNhoodGraphDA(
  liver_milo2, milo_res,
  subset.nhoods = subset.nhoods,
  size_range=c(2,5),
  # ) =)[1:(length()-1)], 
  alpha = 0.1
  )  +
   theme(legend.text = element_text(size=22), legend.title = element_text(size=24))
  
(chol_umap + chol_gr ) + 
  plot_layout(widths = c(1,2), 
                guides = "collect"
                )

# fig4_bright1 +
#   ggsave("~/milo_output/liver_endoGraph.pdf", width=9, height = 5)  

# ggsave("~/dropbox-vu/temp/milo_output/liver_endoGraph.pdf", width=9, height = 5)  

Differential Gene Expression analysis

In a subset of lineages, we want to test for differential expression between neighbourhoods enriched in cirrhotic cells and neighbourhoods enriched

NOTE: Code below here won’t work unless hvgs is defined

DRT: stopping run-through here

Should be able to run everything above.

Add nhood expression to speed-up plotting of heatmaps

liver_milo <- calcNhoodExpression(liver_milo, assay = "logcounts", subset.row = hvgs)

Endothelia

Rebuttal figure showcasing grouping

set.seed(42)
milo_res_endogroups <- groupNhoods(liver_milo, milo_res, max.lfc.delta = 2, overlap = 1)

Found 1385 DA neighbourhoods at FDR 10%
nhoodAdjacency found - using for nhood grouping

p1 <- plotNhoodGroups(liver_milo, milo_res_endogroups, 
                size_range=c(1,3)) 

milo_res_endogroups <- annotateNhoods(liver_milo, milo_res_endogroups, 'annotation_lineage')

p2 <- plotDAbeeswarm(milo_res_endogroups, group.by = 'NhoodGroup') +
  facet_grid(annotation_lineage~., scales="free", space="free")

Converting group_by to factor...

## Plot expression in T cell neighbourhoods
markers_df <- read_csv("~/mount/gdrive/milo/STable3_Ramachandran.csv")

Error: '~/mount/gdrive/milo/STable3_Ramachandran.csv' does not exist.

Group endothelial cells by logFC and DA results

Calculate marker genes between the two groups

Visualize as volcano

Visualize as heatmap

(gene expression values are scaled between 0 and 1 for each gene)

GO term analysis

Cholangiocytes

Calculate marker genes between the two groups

Visualize as volcano

GO term analysis

---

Assemble figure

Assemble supplementary figure